Departmental Performance Improvement
through Data Analytics - Because "what gets measured
gets managed!"
Any serious process improvement effort requires a deep
knowledge and understanding of process data, often beyond
that which is typically gathered and reported. Recent
incursions of process improvement concepts such as Six
Sigma and Lean have brought with them the realization
that process data is critical to improvement. As IT
and manual collection systems generate increasingly
more data, the ability to rapidly analyze, understand,
and synthesize the data escalates.
However, data does not always yield useful
information and insights. Data must be converted
into usable information that will
assist management initiatives and improvement efforts.
This transformation may require multiple toolsets and
technologies, which must be sophisticated enough to
be valuable, yet simple enough to be used by line management
and other non-IT staff. Additionally, better decisioning
at the line-manager and upper-management levels requires
an understanding of what information is needed and in
what format, such that outputs yield the most insight.
Furthermore, the transformation from data to information
requires an understanding of the best means for presentation
of information, such that management can quickly assess
the reported information without struggle and difficulty
of interpretation or extrapolation.
Additionally, historical data, even the best
of data, can only tell us what has already happened.
The ability to use data to predict the future is also
needed. Without effective tools for assessing and understanding
change and potential future states of performance, historical
data alone has limited value in predicting future performance.
Thus simulation
tools are needed to not only analyze and understand
current systems but also future potential performance
to maximize the value of collected data.
These
two issues require a “strategic” approach
to data gathering and analysis to ensure efficient and
effective conversion of data into knowledge and insights.
As leaders in Healthcare
simulation, ProModel has developed detailed data
requirements, analytical tools, and standardized gathering
methodologies that assist our clients in understanding
their data needs and deficiencies, assessing current
operations, analyzing alternative operational models,
and performing true continuous process improvement.
Since simulations require detailed datasets, and since
our clients often need assistance in analyzing their
data to support decision making, ProModel’s expertise
in Data Analytics has been a natural evolution. Even
if no simulations are initially involved or envisioned,
ProModel’s Data Analytics offers tremendous insights
into process and operational issues and opportunities
by turning data into powerful, useable information.
Thus, Data Analytics has become an invaluable “first
step” towards both the initial understanding of
current data and metrics and eventually the simulation
and optimization of operations and processes.
• Evaluate the quality and quantity of existing
data as it relates to process analysis, simulation,
and ongoing improvement efforts,
• Pinpoint any data “gaps” and the
requirements and necessary procurement methodologies
for filling those gaps,
• Develop proper robust data sets for initial
and future analysis,
• Develop ongoing data streams for continuous
process improvement efforts, and data-feeds for simulation
models, and
• Develop effective and insightful reporting functionality
and capabilities to ensure efficient management assessment
and effective management decisioning.
Even if management is satisfied with its current data
reporting, ProModel’s Data Analytics can fine-tune
data feeds for future assessments of specific metrics
and use in simulation models. This is rarely achievable
without assistance and intervention, since knowledge
of simulation as well as clinical applications is required.
The Model below is an example of how to use simulation
to compare three alternative methods for the same medical
procedure to determine the most effective one.
